Evidence map›Paper›PMID 31408649›Full record

ArticleJournal of neuroscience methods2019

Post-acquisition processing confounds in brain volumetric quantification of white matter hyperintensities.

Ahmed A Bahrani, Omar M Al-Janabi, Erin L Abner, Shoshana H Bardach, Richard J Kryscio, Donna M Wilcock, Charles D Smith, Gregory A Jicha

Abstract read
In one paragraph

Article in Journal of neuroscience methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. How often does white matter hyperintensity volume regress in cerebral small vessel disease?International journal of stroke : official journal of the International Stroke Society · 2023
    Article
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ahmed A BahraniDepartment of Biomedical Engineering, College of Engineering, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq.
Omar M Al-JanabiDepartment of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States.
Erin L AbnerDepartment of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States.
Shoshana H BardachDepartment of Gerontology, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States.
Richard J KryscioDepartment of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Department of Statistics, College of Arts and Science, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States.
Donna M WilcockDepartment of Physiology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States.
Charles D SmithDepartment of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Magnetic Resonance Imaging and Spectroscopy Center (MRISC), College of Medicine, University of Kentucky, Lexington, KY, 40506, United States.
Gregory A JichaDepartment of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States. Electronic address: gregory.jicha@uky.edu.

Funding

University of Kentucky Alzheimer's Disease Core Center - Biomarker CoreP30AG028383 · NIA · UNIVERSITY OF KENTUCKY · PI GOLD, BRIAN TIMOTHY, VAN ELDIK, LINDA J · 2006 to 2020
$23.3M
Small vessel disease biomarkers in a longitudinally-followed "stroke-belt" cohortUH3NS100606 · NINDS · UNIVERSITY OF KENTUCKY · PI JICHA, GREGORY A, WILCOCK, DONNA M · 2018 to 2020
$3.4M
Novel pathogenetic mechanism for hippocampal sclerosis, a common Alzheimers mimicR01AG057187 · NIA · UNIVERSITY OF KENTUCKY · PI NELSON, PETER T. · 2017 to 2021
$2.2M
Early Detection and Prevention of Mild Cognitive Impairment due to Cerebrovascular DiseaseR01NR014189 · NINR · UNIVERSITY OF KENTUCKY · PI JICHA, GREGORY A, MOSER, DEBRA KAY · 2012 to 2016
$2.0M
Modulation of microRNA pathways by gemfibrozil in predementia Alzheimer diseaseR01AG042419 · NIA · UNIVERSITY OF KENTUCKY · PI JICHA, GREGORY A, NELSON, PETER T. · 2013 to 2016
$1.6M
Small vessel disease biomarkers in a longitudinally-followed "stroke-belt" cohortUH2NS100606 · NINDS · UNIVERSITY OF KENTUCKY · PI JICHA, GREGORY A, WILCOCK, DONNA M · 2016 to 2017
$1.4M
NIA NIH HHS P30 AG028383NIA NIH HHS R01 AG042419NIA NIH HHS R01 AG057187NINDS NIH HHS UH2 NS100606NINDS NIH HHS UH3 NS100606NINR NIH HHS R01 NR014189
6 · The paper itself

Abstract

backgroundDisparate research sites using identical or near-identical magnetic resonance imaging (MRI) acquisition techniques often produce results that demonstrate significant variability regarding volumetric quantification of white matter hyperintensities (WMH) in the aging population. The sources of such variability have not previously been fully explored. NEW

method3D FLAIR sequences from a group of randomly selected aged subjects were analyzed to identify sources-of-variability in post-acquisition processing that can be problematic when comparing WMH volumetric data across disparate sites. The methods developed focused on standardizing post-acquisition protocol processing methods to develop a protocol with less than 0.5% inter-rater variance.

resultsA series of experiments using standard MRI acquisition sequences explored post-acquisition sources-of-variability in the quantification of WMH volumetric data. Sources-of-variability included: the choice of image center, software suite and version, thresholding selection, and manual editing procedures (when used). Controlling for the identified sources-of-variability led to a protocol with less than 0.5% variability between independent raters in post-acquisition WMH volumetric quantification. COMPARISON WITH EXISTING METHOD(S): Post-acquisition processing techniques can introduce an average variance approaching 15% in WMH volume quantification despite identical scan acquisitions. Understanding and controlling for such sources-of-variability can reduce post-acquisition quantitative image processing variance to less than 0.5%. DISCUSSION: Considerations of potential sources-of-variability in MRI volume quantification techniques and reduction in such variability is imperative to allow for reliable cross-site and cross-study comparisons.

Indexed as

AgedAged, 80 and overAgingBrainFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleNeuroimagingWhite MatterCerebrovascular diseaseSources of variabilityVolumetric analysisWhite matter hyperintensity

Identifiers

PMID31408649
PMCPMC6746343

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.